Abstract
Computational techniques encompass all informatic tools that extract information from large databases. Some remarkable applications include chemoinformatics, geoinformatics, and bioinformatics, involving advanced computing tools to explore and analyse chemical, geological and biological data. Mass spectrometry has provided tremendous input regarding useful instrumentations that enhance the relevance of computational methods. Here, we reviewed computational methods for advanced mass spectrometry particularly the OMIC which comprises of technologies that measure some characteristics of a large family of cellular molecules, such as genes, proteins and small metabolites.
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Research ethics: Not applicable.
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Informed consent: Informed consent was obtained from all individuals included in this write up.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
References
1. Chen, C, Hou, J, Tanner, JJ, Cheng, J. Bioinformatics methods for mass spectrometry-based proteomics data analysis. Int J Mol Sci 2020;21:2873. https://doi.org/10.3390/ijms21082873.Search in Google Scholar PubMed PubMed Central
2. Wang, M, Carver, JJ, Phelan, VV, Sanchez, LM, Garg, N, Peng, Y, et al.. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 2016;34:828–37. https://doi.org/10.1038/nbt.3597.Search in Google Scholar PubMed PubMed Central
3. Engskog, MK, Haglöf, J, Arvidsson, T, Pettersson, C. LC–MS based global metabolite profiling: the necessity of high data quality. Metabolomics 2016;12:1–19.10.1007/s11306-016-1058-xSearch in Google Scholar
4. Zhang, W, Zhao, PX. Quality evaluation of extracted ion chromatograms and chromatographic peaks in liquid chromatography/mass spectrometry-based metabolomics data. In: BMC bioinformatics. London: Springer; 2014:1–13 pp.10.1186/1471-2105-15-S11-S5Search in Google Scholar PubMed PubMed Central
5. Pirttilä, K, Balgoma, D, Rainer, J, Pettersson, C, Hedeland, M, Brunius, C. Comprehensive peak characterization (CPC) in untargeted LC–MS analysis. Metabolites 2022;12:137. https://doi.org/10.3390/metabo12020137.Search in Google Scholar PubMed PubMed Central
6. Smith, CA, Want, EJ, O’maille, G, Abagyan, R, Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 2006;78:779–87. https://doi.org/10.1021/ac051437y.Search in Google Scholar PubMed
7. Benton, HP, Wong, DM, Trauger, SA, Siuzdak, G. XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization. Anal Chem 2008;80:6382–9. https://doi.org/10.1021/ac800795f.Search in Google Scholar PubMed PubMed Central
8. Katajamaa, M, Miettinen, J, Orešič, M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 2006;22:634–6. https://doi.org/10.1093/bioinformatics/btk039.Search in Google Scholar PubMed
9. Pluskal, T, Castillo, S, Villar-Briones, A, Orešič, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinf 2010;11:1–11. https://doi.org/10.1186/1471-2105-11-395.Search in Google Scholar PubMed PubMed Central
10. Pluskal, T, Korf, A, Smirnov, A, Schmid, R, Fallon, TR, Du, X, et al.. Metabolomics data analysis using MZmine. In: Winkler, R, editor. Processing Metabolomics and Proteomics Data with Open Software: A Practical Guide. Cambridge: The Royal Society of Chemistry; 2020.10.1039/9781788019880-00232Search in Google Scholar
11. Binanto, I, Warnars, HLHS, Sianipar, NF, Abbas, BS. LC-MS analysis: mini review frequently used open source softwares. In: 2019 6th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE). IEEE; 2019:1–5 pp.10.1109/ICITACEE.2019.8904162Search in Google Scholar
12. Damiani, T, Heuckeroth, S, Smirnov, A, Mokshyna, O, Brungs, C, Korf, A, et al.. Reproducible mass spectrometry data processing and compound annotation in MZmine 3. Nat Protoc 2024;19:2597–641. https://doi.org/10.1038/s41596-024-00996-y.Search in Google Scholar PubMed
13. Sturm, M, Bertsch, A, Gröpl, C, Hildebrandt, A, Hussong, R, Lange, E, et al.. OpenMS–an open-source software framework for mass spectrometry. BMC Bioinf 2008;9:1–11.10.1186/1471-2105-9-163Search in Google Scholar PubMed PubMed Central
14. Röst, HL, Sachsenberg, T, Aiche, S, Bielow, C, Weisser, H, Aicheler, F, et al.. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods 2016;13:741–8. https://doi.org/10.1038/nmeth.3959.Search in Google Scholar PubMed
15. Perez-Riverol, Y, Wang, R, Hermjakob, H, Müller, M, Vesada, V, Vizcaíno, JA. Open source libraries and frameworks for mass spectrometry based proteomics: a developer’s perspective. Biochim Biophys Acta Proteins Proteom 2014;1844:63–76. https://doi.org/10.1016/j.bbapap.2013.02.032.Search in Google Scholar PubMed PubMed Central
16. Alka, O, Sachsenberg, T, Bichmann, L, Pfeuffer, J, Weisser, H, Wein, S, et al.. OpenMS for open source analysis of mass spectrometric data. PeerJ Prepr 2019;7:e27766v1.10.7287/peerj.preprints.27766v1Search in Google Scholar
17. Alka, O, Sachsenberg, T, Bichmann, L, Pfeuffer, J, Weisser, H, Wein, S, et al.. OpenMS and KNIME for mass spectrometry data processing. In: Winkler, R, editor. Processing metabolomics and proteomics data with Open software: a practical guide. Cambridge: Royal Society of Chemistry; 2020:28–56 pp.10.1039/9781788019880-00201Search in Google Scholar
18. Pfeuffer, J, Bielow, C, Wein, S, Jeong, K, Netz, E, Walter, A, et al.. OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data. Nat Methods 2024:1–3. https://doi.org/10.1038/s41592-024-02197-7.Search in Google Scholar PubMed
19. Tsugawa, H, Cajka, T, Kind, T, Ma, Y, Higgins, B, Ikeda, K, et al.. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 2015;12:523–6. https://doi.org/10.1038/nmeth.3393.Search in Google Scholar PubMed PubMed Central
20. Schlotterbeck, J, Chatterjee, M, Gawaz, M, Lämmerhofer, M. Comprehensive MS/MS profiling by UHPLC-ESI-QTOF-MS/MS using SWATH data-independent acquisition for the study of platelet lipidomes in coronary artery disease. Anal Chim Acta 2019;1046:1–15. https://doi.org/10.1016/j.aca.2018.08.060.Search in Google Scholar PubMed
21. Blaženović, I, Kind, T, Ji, J, Fiehn, O. Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Metabolites 2018;8:31. https://doi.org/10.3390/metabo8020031.Search in Google Scholar PubMed PubMed Central
22. Lai, Z, Tsugawa, H, Wohlgemuth, G, Mehta, S, Mueller, M, Zheng, Y, et al.. Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nat Methods 2018;15:53–6. https://doi.org/10.1038/nmeth.4512.Search in Google Scholar PubMed PubMed Central
23. Tsugawa, H, Ikeda, K, Takahashi, M, Satoh, A, Mori, Y, Uchino, H, et al.. A lipidome atlas in MS-DIAL 4. Nat Biotechnol 2020a;38:1159–63. https://doi.org/10.1038/s41587-020-0531-2.Search in Google Scholar PubMed
24. Tsugawa, H, Ikeda, K, Takahashi, M, Satoh, A, Mori, Y, Uchino, H, et al.. MS-DIAL 4: accelerating lipidomics using an MS/MS, CCS, and retention time atlas. BioRxiv 2020b. 2020.02. 11.944900.10.1101/2020.02.11.944900Search in Google Scholar
25. Yin, Y, Wang, R, Cai, Y, Wang, Z, Zhu, Z-J. DecoMetDIA: deconvolution of multiplexed MS/MS spectra for metabolite identification in SWATH-MS-based untargeted metabolomics. Anal Chem 2019;91:11897–904. https://doi.org/10.1021/acs.analchem.9b02655.Search in Google Scholar PubMed
26. Li, H, Cai, Y, Guo, Y, Chen, F, Zhu, Z-J. MetDIA: targeted metabolite extraction of multiplexed MS/MS spectra generated by data-independent acquisition. Anal Chem 2016;88:8757–64. https://doi.org/10.1021/acs.analchem.6b02122.Search in Google Scholar PubMed
27. Gengbo, C. Development of computational methods for mass spectrometry-based untargeted metabolomics data analysis. Anal Chem 2017;89:4897–906.Search in Google Scholar
28. Ji, H, Lu, H, Zhang, Z. Deep learning enable untargeted metabolite extraction from high throughput coverage data-independent acquisition. BioRxiv 2020. https://doi.org/10.1101/2020.03.22.002683.Search in Google Scholar
29. Emmott, E, Wise, H, Loucaides, EM, Matthews, DA, Digard, P, Hiscox, JA. Quantitative proteomics using SILAC coupled to LC− MS/MS reveals changes in the nucleolar proteome in influenza A virus-infected cells. J Proteome Res 2010;9:5335–45. https://doi.org/10.1021/pr100593g.Search in Google Scholar PubMed
30. Wang, X, He, Y, Ye, Y, Zhao, X, Deng, S, He, G, et al.. SILAC–based quantitative MS approach for real-time recording protein-mediated cell-cell interactions. Sci Rep 2018;8:8441. https://doi.org/10.1038/s41598-018-26262-2.Search in Google Scholar PubMed PubMed Central
31. Kunowska, N, Rotival, M, Yu, L, Choudhary, J, Dillon, N. Identification of protein complexes that bind to histone H3 combinatorial modifications using super-SILAC and weighted correlation network analysis. Nucleic Acids Res 2015;43:1418–32. https://doi.org/10.1093/nar/gku1350.Search in Google Scholar PubMed PubMed Central
32. Tan, TCJ, Spanos, C, Tollervey, D. Improved detection and consistency of RNA-interacting proteomes using DIA SILAC. Nucleic Acids Res 2024;52:e21. https://doi.org/10.1093/nar/gkad1249.Search in Google Scholar PubMed PubMed Central
33. Pino, LK, Baeza, J, Lauman, R, Schilling, B, Garcia, BA. Improved SILAC quantification with data-independent acquisition to investigate bortezomib-induced protein degradation. J Proteome Res 2021;20:1918–27. https://doi.org/10.1021/acs.jproteome.0c00938.Search in Google Scholar PubMed PubMed Central
34. Wang, M, Carver, JJ, Phelan, VV, Sanchez, LM, Garg, N, Peng, Y, et al.. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat Biotechnol 2016;34:828–37. https://doi.org/10.1038/nbt.3597.Search in Google Scholar PubMed PubMed Central
35. Aron, AT, Gentry, EC, Mcphail, KL, Nothias, L-F, Nothias-Esposito, M, Bouslimani, A, et al.. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat Protoc 2020;15:1954–91. https://doi.org/10.1038/s41596-020-0317-5.Search in Google Scholar PubMed
36. Qin, G-F, Zhang, X, Zhu, F, Huo, Z-Q, Yao, Q-Q, Feng, Q, et al.. MS/MS-based molecular networking: an efficient approach for natural products dereplication. Molecules 2022;28:157. https://doi.org/10.3390/molecules28010157.Search in Google Scholar PubMed PubMed Central
37. Petras, D, Phelan, VV, Acharya, D, Allen, AE, Aron, AT, Bandeira, N, et al.. GNPS Dashboard: collaborative exploration of mass spectrometry data in the web browser. Nat Methods 2022;19:134–6. https://doi.org/10.1038/s41592-021-01339-5.Search in Google Scholar PubMed PubMed Central
38. Li, Y, Cui, Z, Li, Y, Gao, J, Tao, R, Li, J, et al.. Integrated molecular networking strategy enhance the accuracy and visualization of components identification: a case study of Ginkgo biloba leaf extract. J Pharmaceut Biomed Anal 2022;209:114523. https://doi.org/10.1016/j.jpba.2021.114523.Search in Google Scholar PubMed
39. Ruttkies, C, Schymanski, EL, Wolf, S, Hollender, J, Neumann, S. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J Cheminf 2016;8:1–16. https://doi.org/10.1186/s13321-016-0115-9.Search in Google Scholar PubMed PubMed Central
40. Wolf, S, Schmidt, S, Müller-Hannemann, M, Neumann, S. In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinf 2010;11:1–12.10.1186/1471-2105-11-148Search in Google Scholar PubMed PubMed Central
41. Neumann, S, Böcker, S. Computational mass spectrometry for metabolomics: identification of metabolites and small molecules. Anal Bioanal Chem 2010;398:2779–88. https://doi.org/10.1007/s00216-010-4142-5.Search in Google Scholar PubMed
42. Ruttkies, C, Neumann, S, Posch, S. Improving MetFrag with statistical learning of fragment annotations. BMC Bioinf 2019;20:376. https://doi.org/10.1186/s12859-019-2954-7.Search in Google Scholar PubMed PubMed Central
43. Kostyukevich, Y, Sosnin, S, Osipenko, S, Kovaleva, O, Rumiantseva, L, Kireev, A, et al.. PyFragMS─A web tool for the investigation of the collision-induced fragmentation pathways. ACS Omega 2022;7:9710–19. https://doi.org/10.1021/acsomega.1c07272.Search in Google Scholar PubMed PubMed Central
44. Sanches, PH, Oliveira, DCD, Reis, IGD, Fernandes, AM, Silva, AA, Eberlin, MN, et al.. Fitting structure-data files (SDF) libraries to progenesis QI identification searches. J Braz Chem Soc 2023;34:1013–19.10.21577/0103-5053.20230016Search in Google Scholar
45. Liao, J, Zhang, Y, Zhang, W, Zeng, Y, Zhao, J, Zhang, J, et al.. Different software processing affects the peak picking and metabolic pathway recognition of metabolomics data. J Chromatogr A 2023;1687:463700. https://doi.org/10.1016/j.chroma.2022.463700.Search in Google Scholar PubMed
46. Zhang, Y, Liao, J, Le, W, Wu, G, Zhang, W. Improving the data quality of untargeted metabolomics through a targeted data-dependent acquisition based on an inclusion list of differential and preidentified ions. Anal Chem 2023;95:12964–73. https://doi.org/10.1021/acs.analchem.3c02888.Search in Google Scholar PubMed
47. Zhong, H, Fang, C, Fan, Y, Lu, Y, Wen, B, Ren, H, et al.. Lipidomic profiling reveals distinct differences in plasma lipid composition in healthy, prediabetic, and type 2 diabetic individuals. GigaScience 2017;6:gix036. https://doi.org/10.1093/gigascience/gix036.Search in Google Scholar PubMed PubMed Central
48. García, CJ, Yang, X, Huang, D, Tomás-Barberán, FA. Can we trust biomarkers identified using different non-targeted metabolomics platforms? Multi-platform, inter-laboratory comparative metabolomics profiling of lettuce cultivars via UPLC-QTOF-MS. Metabolomics 2020;16:1–15. https://doi.org/10.1007/s11306-020-01705-y.Search in Google Scholar PubMed
49. Dynamics, N. What’s new in the latest release of progenesis QI for proteomics v3.0? https://www.nonlinear.com/progenesis/qi-for-proteomics/v3.0/faq/whats-new-in-the-latest-release.aspx [Accessed 22 Jun 2024].Search in Google Scholar
50. Cook-Botelho, JC, Bachmann, LM, French, D. Chapter 10 - steroid hormones. In: Nair, H, Clarke, W, editors. Mass spectrometry for the clinical laboratory. San Diego: Academic Press; 2017.10.1016/B978-0-12-800871-3.00010-9Search in Google Scholar
51. Neagu, AN, Jayathirtha, M, Baxter, E, Donnelly, M, Petre, BA, Darie, CC. Applications of tandem mass spectrometry (MS/MS) in protein analysis for biomedical research. Molecules 2022;27. https://doi.org/10.3390/molecules27082411.Search in Google Scholar PubMed PubMed Central
52. Calamai, L, Villanelli, F, Bartolucci, G, Pieraccini, G, Moneti, G. 4.24 - sample preparation for direct MS analysis of food. In: Pawliszyn, J, editor. Comprehensive sampling and sample preparation. Oxford: Academic Press; 2012.10.1016/B978-0-12-381373-2.00148-4Search in Google Scholar
53. Allison, TM, Barran, P, Cianférani, S, Degiacomi, MT, Gabelica, V, Grandori, R, et al.. Computational strategies and challenges for using native ion mobility mass spectrometry in biophysics and structural biology. Anal Chem 2020;92:10872–80. https://doi.org/10.1021/acs.analchem.9b05791.Search in Google Scholar PubMed
54. Lee, C-W, Su, H, Shiea, J. Potential applications and challenges of novel ambient ionization mass spectrometric techniques in the emergency care for acute poisoning. TrAC, Trends Anal Chem 2022;157:116742. https://doi.org/10.1016/j.trac.2022.116742.Search in Google Scholar
55. Djambazova, KV, Van Ardenne, JM, Spraggins, JM. Advances in imaging mass spectrometry for biomedical and clinical research. TrAC, Trends Anal Chem 2023;169:117344. https://doi.org/10.1016/j.trac.2023.117344.Search in Google Scholar PubMed PubMed Central
56. Arentz, G, Mittal, P, Zhang, C, Ho, YY, Briggs, M, Winderbaum, L, et al.. Chapter two - applications of mass spectrometry imaging to cancer. In: Drake, RR, Mcdonnell, LA, editors. Advances in cancer research. Cambridge, MA: Academic Press; 2017.10.1016/bs.acr.2016.11.002Search in Google Scholar PubMed
57. Dettmer, K, Aronov, PA, Hammock, BD. Mass spectrometry-based metabolomics. Mass Spectrom Rev 2007;26:51–78. https://doi.org/10.1002/mas.20108.Search in Google Scholar PubMed PubMed Central
58. Gowda, GA, Djukovic, D. Overview of mass spectrometry-based metabolomics: opportunities and challenges. Methods Mol Biol 2014;1198:3–12. https://doi.org/10.1007/978-1-4939-1258-2_1.Search in Google Scholar PubMed PubMed Central
59. Jiang, Y, Rex, Da. B, Schuster, D, Neely, BA, Rosano, GL, Volkmar, N, et al.. Comprehensive overview of bottom-up proteomics using mass spectrometry. ACS Measurement Science Au 2024;4:338–417.10.1021/acsmeasuresciau.3c00068Search in Google Scholar PubMed PubMed Central
60. Thadhani, VM, Musharraf, SG, Ali, A. Sensitive analysis of secondary metabolites in different lichen species using liquid chromatography–mass spectrometry: a review. Stud Nat Prod Chem 2021;70:23–49. https://doi.org/10.1016/b978-0-12-819489-8.00007-7.Search in Google Scholar
61. Castillo, S, Gopalacharyulu, P, Yetukuri, L, Orešič, M. Algorithms and tools for the preprocessing of LC–MS metabolomics data. Chemometr Intell Lab Syst 2011;108:23–32. https://doi.org/10.1016/j.chemolab.2011.03.010.Search in Google Scholar
62. Kösters, M, Leufken, J, Leidel, SA. SMITER–a python library for the simulation of LC-MS/MS experiments. Genes 2021;12:396. https://doi.org/10.3390/genes12030396.Search in Google Scholar PubMed PubMed Central
63. Politis, A, Schmidt, C. Structural characterisation of medically relevant protein assemblies by integrating mass spectrometry with computational modelling. J Proteonomics 2018;175:34–41. https://doi.org/10.1016/j.jprot.2017.04.019.Search in Google Scholar PubMed
64. Oladipupo, AR, Alaribe, SCA, Ogunlaja, AS, Beniddir, MA, Gordon, AT, Ogah, CO, et al.. Structure-based molecular networking, molecular docking, dynamics simulation and pharmacokinetic studies of Olax subscorpioidea for identification of potential inhibitors against selected cancer targets. J Biomol Struct Dyn 2023;8:1–16. https://doi.org/10.1080/07391102.2023.2198032.Search in Google Scholar PubMed
65. Griffiths, WJ, Wang, Y. Mass spectrometry: from proteomics to metabolomics and lipidomics. Chem Soc Rev 2009;38:1882–96. https://doi.org/10.1039/b618553n.Search in Google Scholar PubMed
66. Beccaria, M, Cabooter, D. Current developments in LC-MS for pharmaceutical analysis. Analyst 2020;145:1129–57. https://doi.org/10.1039/c9an02145k.Search in Google Scholar PubMed
67. Barceló, D, Petrovic, M. Challenges and achievements of LC-MS in environmental analysis: 25 years on. TrAC, Trends Anal Chem 2007;26:2–11. https://doi.org/10.1016/j.trac.2006.11.006.Search in Google Scholar
68. López-Ruiz, R, Romero-González, R, Frenich, AG. Ultrahigh-pressure liquid chromatography-mass spectrometry: an overview of the last decade. TrAC, Trends Anal Chem 2019;118:170–81. https://doi.org/10.1016/j.trac.2019.05.044.Search in Google Scholar
69. Furey, A, Moriarty, M, Bane, V, Kinsella, B, Lehane, M. Ion suppression; a critical review on causes, evaluation, prevention and applications. Talanta 2013;115:104–22. https://doi.org/10.1016/j.talanta.2013.03.048.Search in Google Scholar PubMed
70. Usman, AG, Işik, S, Abba, SI. Qualitative prediction of Thymoquinone in the high-performance liquid chromatography optimization method development using artificial intelligence models coupled with ensemble machine learning. Sep Sci Plus 2022;5:579–87. https://doi.org/10.1002/sscp.202200071.Search in Google Scholar
71. Moayedpour, S, Parastar, H. RMet: an automated R based software for analyzing GC-MS and GC× GC-MS untargeted metabolomic data. Chemometr Intell Lab Syst 2019;194:103866. https://doi.org/10.1016/j.chemolab.2019.103866.Search in Google Scholar
72. Domingo-Almenara, X, Brezmes, J, Vinaixa, M, Samino, S, Ramirez, N, Ramon-Krauel, M, et al.. eRah: a computational tool integrating spectral deconvolution and alignment with quantification and identification of metabolites in GC/MS-based metabolomics. Anal Chem 2016;88:9821–9. https://doi.org/10.1021/acs.analchem.6b02927.Search in Google Scholar PubMed
73. Thaveesangsakulthai, I, Kulsing, C. Using a spreadsheet-based simulation to practice and evaluate iterative column selection and experimental design in chemical fingerprinting with GC–MS. Washington, DC: ACS Publications; 2021.10.1021/acs.jchemed.0c01351Search in Google Scholar
74. Schymanski, EL, Meringer, M, Brack, W. Automated strategies to identify compounds on the basis of GC/EI-MS and calculated properties. Anal Chem 2011;83:903–12. https://doi.org/10.1021/ac102574h.Search in Google Scholar PubMed
75. Lebanov, L, Ghiasvand, A, Paull, B. Data handling and data analysis in metabolomic studies of essential oils using GC-MS. J Chromatogr A 2021;1640:461896. https://doi.org/10.1016/j.chroma.2021.461896.Search in Google Scholar PubMed
76. Littlewood, AB. Gas chromatography: principles, techniques, and applications. Amsterdam: Elsevier; 2013.Search in Google Scholar
77. Bouziani, A, Yahya, M. Mass spectrometry coupled with chromatography toward separation and identification of organic mixtures. In: Biodegradation technology of organic and inorganic pollutants. London: IntechOpen; 2021.10.5772/intechopen.100517Search in Google Scholar
78. Jeffery, G. Vogel’s textbook of quantitative chemical analysis, 5th ed. Hoboken, NJ: A John Wiley & Sons, INC; 2022.Search in Google Scholar
79. Stashenko, EE, Martı́Nez, JR. Derivatization and solid-phase microextraction. TrAC, Trends Anal Chem 2004;23:553–61. https://doi.org/10.1016/j.trac.2004.06.002.Search in Google Scholar
80. Hussain, SZ, Maqbool, K. GC-MS: principle, technique and its application in food science. Int J Curr Sci 2014;13:116–26.Search in Google Scholar
81. Eckenrode, B. Environmental and forensic applications of field-portable GC-MS: an overview. J Am Soc Mass Spectrom 2001;12:683–93. https://doi.org/10.1016/s1044-0305(01)00251-3.Search in Google Scholar PubMed
82. Zanella, D, Focant, JF, Franchina, FA. 30th Anniversary of comprehensive two-dimensional gas chromatography: latest advances. Anal Sci Adv 2021;2:213–24. https://doi.org/10.1002/ansa.202000142.Search in Google Scholar PubMed PubMed Central
83. Snow, N. Flying high with sensitivity and selectivity: GC–MS to GC–MS/MS. LCGC NA 2021;39:222–8. https://doi.org/10.56530/lcgc.na.yn3065q6.Search in Google Scholar
84. Špánik, I, Machyňáková, A. Recent applications of gas chromatography with high-resolution mass spectrometry. J Separ Sci 2018;41:163–79. https://doi.org/10.1002/jssc.201701016.Search in Google Scholar PubMed
85. Matheis, K, Fuchs, B, Lemmnitzer, K, Süss, R, Griesinger, H, Minarik, S, et al.. Combining TLC separation with MS detection-a revival of TLC. J Glycom Lipidom 2015;5:1.Search in Google Scholar
86. Bele, AA, Khale, A. An overview on thin layer chromatography. Int J Pharmaceut Sci Res 2011;2:256.Search in Google Scholar
87. Awad, H, Khamis, MM, El-Aneed, A. Mass spectrometry, review of the basics: ionization & quot. Appl Spectrosc Rev 2015;50:158–75.10.1080/05704928.2014.954046Search in Google Scholar
88. Bagócsi, B, Fábián, D, Laukó, A, Mezei, M, Mahó, S, Végh, Z, et al.. Comparison of OPLC and other chromatographic methods (TLC, HPLC, and GC) for in-process purity testing of nandrolone. JPC – J Planar Chromatogr – Mod TLC 2002;15:252–7. https://doi.org/10.1556/JPC.15.2002.4.2.Search in Google Scholar
89. Tuzimski, T, Sherma, J. Thin-layer chromatography and mass spectrometry for the analysis of lipids. In: Wenk, MR, editor. Encyclopedia of Lipidomics. Dordrecht: Springer Netherlands; 2016.10.1007/978-94-007-7864-1_62-1Search in Google Scholar
90. Borisov, R, Kanateva, A, Zhilyaev, D. Recent advances in combinations of TLC with MALDI and other desorption/ionization mass-spectrometry techniques. Front Chem 2021b;9. https://doi.org/10.3389/fchem.2021.771801.Search in Google Scholar PubMed PubMed Central
91. Kolluri, S, Lin, J, Liu, R, Zhang, Y, Zhang, W. Machine learning and artificial intelligence in pharmaceutical research and development: a review. AAPS J 2022;24:19. https://doi.org/10.1208/s12248-021-00644-3.Search in Google Scholar PubMed PubMed Central
92. Sarirete, A, Balfagih, Z, Brahimi, T, Lytras, MD, Visvizi, A. Artificial intelligence and machine learning research: towards digital transformation at a global scale. J Ambient Intell Hum Comput 2022;13:3319–21. https://doi.org/10.1007/s12652-021-03168-y.Search in Google Scholar
93. Bhole, R, Jagtap, S, Chadar, K, Zambare, Y. Review on hyphenation in HPTLC-MS. Res J Pharm Technol 2020;13:1028–34. https://doi.org/10.5958/0974-360x.2020.00189.4.Search in Google Scholar
94. Engel, KM, Schiller, J. The value of coupling thin-layer chromatography to mass spectrometry in lipid research-a review. J Chromatogr B 2021;1185:123001. https://doi.org/10.1016/j.jchromb.2021.123001.Search in Google Scholar PubMed
95. Verma, KL, Kumar, M, Singh, AP. HPTLC-MS as a neoteric hyphenated technique for separation and forensic identification of drugs. J Anal Sci Methods Instrum 2018;8:1–15. https://doi.org/10.4236/jasmi.2018.81001.Search in Google Scholar
96. Kowalska, T, Sajewicz, M. Thin-layer chromatography (TLC) in the screening of botanicals–its versatile potential and selected applications. Molecules 2022;27:6607. https://doi.org/10.3390/molecules27196607.Search in Google Scholar PubMed PubMed Central
97. Waksmundzka-Hajnos, M, Hawrył, M, Hawrył, A, Jóżwiak, G. Thin layer chromatography in phytochemical analysis. In: Buszewski, B, Baranowska, I, editors. Handbook of bioanalytics. Cham: Springer International Publishing; 2020.Search in Google Scholar
98. Borisov, R, Kanateva, A, Zhilyaev, D. Recent advances in combinations of TLC with MALDI and other desorption/ionization mass-spectrometry techniques. Front Chem 2021a;9:771801. https://doi.org/10.3389/fchem.2021.771801.Search in Google Scholar
99. Sherma, J, Rabel, F. Review of advances in planar chromatography-mass spectrometry published in the period 2015–2019. J Liq Chromatogr Relat Technol 2020;43:394–412. https://doi.org/10.1080/10826076.2020.1725561.Search in Google Scholar
100. Debnath, M, Prasad, G, Bisen, PS. Molecular diagnostics: promises and possibilities. Dordrech Heidelberg London: Springer; 2010:11–31 pp.10.1007/978-90-481-3261-4_2Search in Google Scholar
101. Fischer, HP. Towards quantitative biology: integration of biological information to elucidate disease pathways and to guide drug discovery. Biotechnol Annu Rev 2005;11:1–68. https://doi.org/10.1016/s1387-2656(05)11001-1.Search in Google Scholar PubMed
102. Horgan, RP, Kenny, LC. ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet Gynaecol 2011;13:189–95. https://doi.org/10.1576/toag.13.3.189.27672.Search in Google Scholar
103. Fiers, W, Contreras, R, Duerinck, F, Haegeman, G, Iserentant, D, Merregaert, J, et al.. Complete nucleotide sequence of bacteriophage MS2 RNA: primary and secondary structure of the replicase gene. Nature 1976;260:500–7. https://doi.org/10.1038/260500a0.Search in Google Scholar PubMed
104. Lein, ES, Hawrylycz, MJ, Ao, N, Ayres, M, Bensinger, A, Bernard, A, et al.. Genome-wide atlas of gene expression in the adult mouse brain. Nature 2007;11:168–76. https://doi.org/10.1038/nature05453.Search in Google Scholar PubMed
105. Kanehis, M, Goto, S, Hattori, M, Aoki-Kinoshita, KF, Itoh, M, Kawashima, S, et al.. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 2006;34:D354–357. https://doi.org/10.1093/nar/gkj102.Search in Google Scholar PubMed PubMed Central
106. MacGregor, JT. Biomarkers of cancer risk and therapeutic benefit: new technologies, new opportunities, and some challenges. Toxicol Pathol 2007;1:99–105. https://doi.org/10.1080/01926230490425067.Search in Google Scholar PubMed
107. Anderson, NG, Anderson, NL. Twenty years of two-dimensional electrophoresis: past, present and future. Electrophoresis 1996;17:443–53. https://doi.org/10.1002/elps.1150170303.Search in Google Scholar PubMed
108. Anderson, NL, Anderson, NG. Proteome and proteomics: new technologies, new concepts, and new words. Electrophoresis 1998;19:1853–61. https://doi.org/10.1002/elps.1150191103.Search in Google Scholar PubMed
109. Macaulay, IC, Carr, P, Gusnanto, A, Ouwehand, WH, Fitzgerald, D, Watkins, NA. Platelet genomics and proteomics in human health and disease. J Clin Invest 2005;115:3370–7. https://doi.org/10.1172/JCI26885.Search in Google Scholar PubMed PubMed Central
110. Pandey, A, Fernandez, MM, Stehen, H, Blagoev, B, Nielsen, MM, Roche, S, et al.. Identification of a novel immunoreceptor tyrosine-based activation motif-containing molecule, STAM2, by mass spectrometry and its involvement in growth factor and cytokine receptor signaling pathways. J Biol Chem 2000;275:38633–9. https://doi.org/10.1074/jbc.m007849200.Search in Google Scholar PubMed
111. Banks, RE, Dunn, MJ, Hochstrasser, DF, Sanchez, JC, Blackstock, W, Pappin, DJ, et al.. Proteomics: new perspectives, new biomedical opportunities. Lancet 2000;356:1749–56. https://doi.org/10.1016/S0140-6736(00)03214-1.Search in Google Scholar PubMed
112. Yokoyama, S, Hirota, H, Kigawa, T, Yabuki, T, Shirouzu, M, Terada, T, et al.. Structural genomics projects in Japan. Nat Struct Mol Biol 2000;7:943–5. https://doi.org/10.1038/80712.Search in Google Scholar PubMed
113. Gavin, AC, Bosche, M, Krause, R, Grandi, P, Marzioch, M, Bauer, A, et al.. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002;415:141–7. https://doi.org/10.1038/415141a.Search in Google Scholar PubMed
114. Collins, BC, Aebersold, R. Proteomics goes parallel. Nat Biotechnol 2018;36:1051–3. https://doi.org/10.1038/nbt.4288.Search in Google Scholar PubMed
115. Anderson, NL, Matheson, AD, Steiner, S. Proteomics: applications in basic and applied biology. Curr Opin Biotechnol 2000;11:408–12. https://doi.org/10.1016/s0958-1669(00)00118-x.Search in Google Scholar PubMed
116. Tyers, M, Mann, M. From genomics to proteomics. Nature 2003;422:193–7. https://doi.org/10.1038/nature01510.Search in Google Scholar PubMed
117. Haudek, VJ, Slany, A, Gundacker, NC, Wimmer, H, Drach, J, Gerner, C. Proteome maps of the main human peripheral blood constituents. J Proteome Res 2009;8:3834–43. https://doi.org/10.1021/pr801085g.Search in Google Scholar PubMed
118. Alsagaby, SA, Alhumaydhi, FA. Proteomics insights into the pathology and prognosis of chronic lymphocytic leukemia. Saudi Med J 2019;40:317. https://doi.org/10.15537/smj.2019.4.23598.Search in Google Scholar PubMed PubMed Central
119. Mikkelsen, SR, Cortón, E. Bioanalytical chemistry. Hoboken, NJ: John Wiley & Sons, Inc.; 2004:224 p.10.1002/0471623628Search in Google Scholar
120. Sparkman, OD. Mass spectrometry desk reference. Pittsburgh: Global View Pub; 2000.Search in Google Scholar
121. Chandrasekhar, K, Dileep, A, Lebonah, DE, Pramoda Kumari, J. A short review on proteomics and its applications. Int Lett Nat Sci 2014;12:77–84. https://doi.org/10.56431/p-avsz0g.Search in Google Scholar
122. Tang, N, Tornatore, P, Weinberger, SR. Current developments in SELDI affinity technology. Mass Spectrom Rev 2004;23:34–44. https://doi.org/10.1002/mas.10066.Search in Google Scholar PubMed
123. Sangha, JS, Yolanda, HC, Kaur, J, Khan, W, Abduljaleel, Z, Alanazi, MS, et al.. Int J Mol Sci 2013;14:3921–45.10.3390/ijms14023921Search in Google Scholar PubMed PubMed Central
124. Johnson, CH, Gonzalez, FJ. Challenges and opportunities of metabolomics. J Cell Physiol 2012;227:2975–81. https://doi.org/10.1002/jcp.24002.Search in Google Scholar PubMed PubMed Central
125. Harrigan, GG, Goodacre, R. Metabolic profiling: its role in biomarker discovery and gene function analysis. Boston: Kluwer Academic Publishers; 2003:335 p.10.1007/978-1-4615-0333-0Search in Google Scholar
126. Wang, JH, Byun, J, Pennathur, S. Analytical approaches to metabolomics and applications to systems biology. Semin Nephrol 2010;30:500–11. https://doi.org/10.1016/j.semnephrol.2010.07.007.Search in Google Scholar PubMed PubMed Central
127. Oladipupo, AR, Alaribe, SCA, Ogunlaja, AS, Beniddir, MA, Ogah, CO, Okpuzor, J, et al.. Chemical and biological insights on Phaulopsis falcisepala: a source of bioactive compounds with multifunctional anticancer potentials. Chem Africa 2022;6:1175–89. https://doi.org/10.1007/s42250-022-00553-8.Search in Google Scholar
128. Hyötyläinen, T, Wiedmer, S. Chromatographic methods in metabolomics. United Kingdom: The Royal Society of Chemistry; 2013.10.1039/9781849737272Search in Google Scholar
129. Wishart, DS, Feunang, YD, Marcu, A, Guo, AC, Liang, K, Vázquez-Fresno, R, et al.. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 2018;46:D608D617. https://doi.org/10.1093/nar/gkx1089.Search in Google Scholar PubMed PubMed Central
130. Liotta, LA, Lowenthal, M, Mehta, A, Conrads, TP, Veenstra, TD, Fishman, DA, et al.. Importance of communication between producers and consumers of publicly available experimental data. J Natl Cancer Inst 2000;97:315–19. https://doi.org/10.1093/jnci/dji053.Search in Google Scholar PubMed
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Articles in the same Issue
- Frontmatter
- Reviews
- Composites in battery casing and energy storage
- On the development of pharmacokinetic models for the characterisation and diagnosis of von Willebrand disease
- Energy efficiency and sustainability in composites industry
- Limitations and future outlooks for char and its composites in energy harvesting application
- Computational methods for advanced mass spectrometry – a review
- A novel strategy for brain cancer treatment through a multiple emulsion system for simultaneous therapeutics delivery
Articles in the same Issue
- Frontmatter
- Reviews
- Composites in battery casing and energy storage
- On the development of pharmacokinetic models for the characterisation and diagnosis of von Willebrand disease
- Energy efficiency and sustainability in composites industry
- Limitations and future outlooks for char and its composites in energy harvesting application
- Computational methods for advanced mass spectrometry – a review
- A novel strategy for brain cancer treatment through a multiple emulsion system for simultaneous therapeutics delivery